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Zero Shot Video Question Answer On Intentqa

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
IG-VLM65.3An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM-
VideoTree (GPT4)66.9VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos-
VidCtx (7B)67.1VidCtx: Context-aware Video Question Answering with Image Models-
LLoVi (GPT-4)64.0A Simple LLM Framework for Long-Range Video Question-Answering-
LangRepo (12B)59.1Language Repository for Long Video Understanding-
SeViLA (4B)60.9Self-Chained Image-Language Model for Video Localization and Question Answering-
LVNet71.1Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA-
ENTER71.5ENTER: Event Based Interpretable Reasoning for VideoQA-
LLoVi (7B)53.6A Simple LLM Framework for Long-Range Video Question-Answering-
Mistral (7B)50.4Mistral 7B-
TS-LLaVA-34B67.9TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models-
SlowFast-LLaVA-34B60.1SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models-
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Zero Shot Video Question Answer On Intentqa | SOTA | HyperAI